Improves unsupervised domain adaptation by mixing source and target domains.
problem Improves unsupervised domain adaptation by mixing source and target domains.
method Enforces training constraints across domains using mixup formulation and feature-level consistency regularizer.
result Significantly improves state-of-the-art performance on image classification and human activity recognition tasks.
TCRI improves domain generalization by enforcing conditional independence constraints.
problem Limitations of existing domain generalization methods due to incomplete constraints.
method TCRI implements regularizers motivated by conditional independence constraints.
result TCRI achieves cross-domain stability and outperforms baselines in worst-domain accuracy.
Paper proposes a new framework for semi-supervised learning.
problem Limited annotated data in supervised learning.
method Fuzzy domain constraint-based framework for semi-supervised learning.
result Enhances model quality for semi-supervised learning.
Study finds multiple solutions to a complex equation with volume constraint.
problem Finding multiple solutions to a nonlinear elliptic equation with a specific potential.
method Analyzes a Van der Waals-Allen-Cahn-Hilliard equation with a linear volume constraint on a bounded Lipschitz domain.
result Estimates the number of solutions using topological and homological invariants.
New constraints rule out some optimal domains for helicity maximisation.
problem Finding a smooth domain of fixed volume that maximizes helicity.
method Established additional geometric constraints on optimal domains.
result Ruled out the optimality of a broad class of solid tori.
Incorporating domain knowledge into the modeling process is an effective way to improve learning accuracy. However, as it is provided by humans, domain knowledge can only be specified with some degree of uncertainty. We propose to explicitly model such uncertainty through probabilistic constraints over the parameter sp…
Domain knowledge helps detect adversarial examples in multi-label classification.
problem Detecting adversarial examples in multi-label classification.
method Convert domain knowledge into constraints and inject them into a semi-supervised learning problem.
result Domain-knowledge constraints help detect adversarial examples effectively.
AFLAC improves domain generalization by balancing invariance and accuracy.
problem Balancing domain invariance and classification accuracy for domain generalization.
method Adversarial feature learning with accuracy constraint (AFLAC).
result AFLAC outperforms domain-invariance-based methods on synthetic and real-world datasets.
Survey of integrating domain knowledge into DL models.
problem Improving DL model performance with limited data or complex functions.
method Five categories of approaches to inject domain knowledge into DL models.
result Survey identifies five main categories of approaches.
Paper improves deep learning for solving evolutionary equations with trainable hard constraints.
problem Low computational accuracy of standard PINNs in large temporal domains.
method Sequential learning strategies and trainable influence functions for hard constraints.
result Significantly improved computational accuracy and universality of the method.
This work integrates domain knowledge into A*-based causal discovery methods.
problem Efficiently incorporating domain knowledge into A*-based causal discovery methods.
method Integrates various types of domain knowledge into A*-based causal discovery methods, reducing the graph search space and improving computational gains.
result Small amounts of domain knowledge can dramatically speed up A*-based causal discovery and improve its performance and practicality.
Study optimizes perimeter in convex domains with anisotropic constraints.
problem Optimizing perimeter in convex domains with anisotropic constraints.
method Analytical properties, topological features, and geometric measure theory results.
result Sharp isoperimetric inequalities and existence of minimizers.
New algorithm clusters data and learns kernels without relaxing constraints.
problem Learning kernels or distance metrics from pairwise constraints without losing generalization.
method Joint clustering and kernel learning without relaxing constraints.
result Outperforms existing approaches on diverse datasets.
New method combines domain changes and sparse mixing for better latent variable learning.
problem Challenges in identifying latent variables due to insufficient domain changes and violated sparsity constraints.
method Combines sufficient changes and sparse mixing constraints, using domain encoding networks and variational autoencoders.
result Identifiability of latent variables achieved with less restrictive constraints.
SIG model identifies invariant variables for MSDA with fewer domain constraints.
problem Challenges in enforcing minimal changes across domains for MSDA.
method Subspace identification theory and variational inference.
result SIG model outperforms existing techniques on various benchmark datasets.
Sigma simplifies collaboration in economics with a streamlined computational representation.
problem Lack of effective collaboration tools in economics for large-scale projects.
method Introduces Sigma, a domain-specific computational representation for economics based on facets, contributions, and constraints of data.
result Sigma enables sharing and formalizing domain-specific concepts in economics for crowd-based scientific investigations.
Solves batch policy learning with constraints using flexible meta-algorithm and OPE.
problem Efficiently use pre-collected behavior data and mediate among competing objectives and constraints.
method Flexible meta-algorithm with any batch RL and online learning subroutines, specific instantiation, and OPE method.
result Achieves strong empirical results and OPE performance in various domains, including car driving.
The paper classifies energy-minimizing sets in specific domains.
problem Classifying volume-constraint local energy-minimizing sets.
method Proved a Poincaré-type inequality for stable sets.
result Relative boundary of energy-minimizing sets is smooth.
Signal recovery is one of the key techniques of Compressive sensing (CS). It reconstructs the original signal from the linear sub-Nyquist measurements. Classical methods exploit the sparsity in one domain to formulate the L0 norm optimization. Recent investigation shows that some signals are sparse in multiple domains.…
Given asymptotically flat initial data on M^3 for the vacuum Einstein field equation, and given a bounded domain in M, we construct solutions of the vacuum constraint equations which agree with the original data inside the given domain, and are identical to that of a suitable Kerr slice (or identical to a member of som…
Novel framework for Bayesian neural networks incorporating task-specific constraints.
problem Task-specific constraints in supervised model deployment.
method Introduces Output-Constrained BNN (OC-BNN) framework.
result OC-BNNs effectively incorporate prior expert knowledge and desiderata like safety and fairness.
The paper shows how Lagrangian duality improves deep learning for constrained problems.
problem Learning optimization problems with complex constraints in science and engineering.
method Lagrangian duality applied to deep learning models.
result Lagrangian duality brings significant benefits for constrained learning tasks.
New method optimizes incentive allocation with budget constraints.
problem Optimizing financial incentives in marketing campaigns with limited feedback.
method Two-step approach: domain adaptation for reward estimation followed by policy optimization.
result Significant improvement in synthetic and real datasets.
Agent learns from an expert, adapting to constraints in concept learning.
problem Insufficient query selection in active learning for realistic human domains.
method Imitation learning to reason about both internal goals and external constraints.
result Agent outperforms other active learners under most constrained conditions.
Neural SDEs model suicide risk with compact state space constraints.
problem Modeling suicide risk with irregular, noisy, and partially observed data.
method Developed neural SDEs confined to compact state spaces, addressing domain constraints and numerical stability.
result Improved forecasts and optimization dynamics over standard models on EMA datasets.
We present an adaptive online gradient descent algorithm to solve online convex optimization problems with long-term constraints , which are constraints that need to be satisfied when accumulated over a finite number of rounds T , but can be violated in intermediate rounds. For some user-defined trade-off parameter β…
New method improves text classification without labeled target data.
problem Improving text classification under domain shift without labeled target data.
method Diversity-based generalization using multi-head attention with diversity constraints.
result Method matches state-of-the-art performance without labeled target data.
New method for sampling on constrained domains using orthogonal-space gradient flow.
problem Sampling on manifolds defined by constraints is challenging.
method Orthogonal-Space Variational Gradient Descent (O-Gradient)
result O-Gradient converges to the target constrained distribution efficiently.
The study explores conjectures on mean convex domains in spaces with scalar curvature constraints.
problem Understanding mean convex domains in spaces with scalar curvature constraints.
method Formulated conjectures and proved theorems.
result Motivated conjectures on mean convex domains.
New algorithms for sampling in constrained domains without learning rates.
problem Sampling in constrained domains with fairness constraints and post-selection inference.
method Coin betting ideas from convex optimisation and a unifying framework for constrained sampling.
result Our algorithms achieve competitive performance without hyperparameter tuning.
New diffusion models handle constrained domains, improving generative tasks.
problem Diffusion models struggle with manifolds defined by inequality constraints.
method Developed two noising processes: logarithmic barrier metric and reflected Brownian motion.
result Demonstrated practical utility on synthetic and real-world tasks.
New algorithm reduces robust optimization scale for better constraint satisfaction.
problem Finding robust solutions to optimization problems with unknown constraints.
method Empirical domain reduction to determine robustness scale.
result Our algorithm's scale is less affected by parameter dimensionality.
This paper focuses on martingale optimal transport problems when the martingales are assumed to have bounded quadratic variation. First, we give a result that characterizes the existence of a probability measure satisfying some convex transport constraints in addition to having given initial and terminal marginals. Sev…
Private algorithms adapt from public to private domains with minimal labeled data.
problem Adapting from a public source domain to a private target domain with few labeled data.
method Differentially private discrepancy minimization algorithms based on Frank-Wolfe and Mirror-Descent methods.
result Effective adaptation with strong generalization and privacy guarantees.
ADMM solves constrained CASH problems by breaking them into smaller, manageable pieces.
problem Handling black-box constraints in CASH problems.
method Leverages ADMM optimization framework to decompose CASH problems.
result ADMM facilitates incorporation of black-box constraints.
Telemonitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are energy consumption, data compression, and device cost. Conventional data compression methodologies, al…
Many engineering problems require identifying feasible domains under implicit constraints. One example is finding acceptable car body styling designs based on constraints like aesthetics and functionality. Current active-learning based methods learn feasible domains for bounded input spaces. However, we usually lack pr…
Meta-gradient D4PG optimizes performance and constraint adherence in RL.
problem Balancing performance and adherence to complex constraints in RL.
method Uses meta-gradients to find a balance between expected return and minimizing constraint violations.
result Meta-gradient D4PG consistently outperforms baselines across MuJoCo domains.
Algorithm mitigates performance loss in constrained reinforcement learning with model misspecification.
problem Performance loss in reinforcement learning policies due to model misspecification in constrained control systems.
method Proposes an algorithm to handle constrained model misspecification in continuous control systems.
result Algorithm successfully mitigates performance loss in real-world reinforcement learning tasks.
The paper examines geometric properties of domains for the p-Laplacian in Euclidean and hyperbolic spaces.
problem Exploring geometric properties of unbounded extremal domains for the p-Laplacian operator.
method Analyzing properties in Euclidean and hyperbolic spaces, proving constraints on domains and their asymptotic boundaries.
result Extremal domains in two dimensions must be balls, and in hyperbolic space, they have specific geometric constraints.
Training a model to perform a task typically requires a large amount of data from the domains in which the task will be applied. However, it is often the case that data are abundant in some domains but scarce in others. Domain adaptation deals with the challenge of adapting a model trained from a data-rich source domai…
RFM improves CNFs by adding a boundary constraint term and matching velocity fields.
problem Flow matching on constrained domains leads to unnatural samples.
method RFM adds a boundary constraint term and matches velocity fields in a simulation-free manner.
result RFM achieves comparable or better results on standard image benchmarks and produces high-quality samples.
MiVaBo optimizes mixed-variable functions efficiently, handling constraints.
problem Optimizing expensive, mixed-variable functions with discrete constraints.
method Combines linear surrogate model and Thompson sampling, optimizing acquisition function.
result First BO method to handle complex constraints over discrete variables.
The work investigates deep generative models, which allow us to use training data from one domain to build a model for another domain. We propose the Variational Bi-domain Triplet Autoencoder (VBTA) that learns a joint distribution of objects from different domains. We extend the VBTAs objective function by the relativ…
Framework improves classifier calibration under differential privacy for domain shift.
problem Improving classifier calibration under domain shift with privacy constraints.
method Differential privacy framework for adapting recalibration algorithms.
result Novel accuracy temperature scaling algorithm outperforms existing methods on private datasets.
SOO uses bandit theory to optimize functions with limited evaluations.
problem Optimizing functions with a limited number of evaluations.
method SOO is a machine learning approach that uses domain partitioning and a tradeoff between exploration and exploitation.
result SOO provides guarantees on the returned solution and is numerically efficient.
Domain adaptation refers to the process of learning prediction models in a target domain by making use of data from a source domain. Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains. In this manuscri…
Paper optimizes stochastic optimization with multiple constraints.
problem Optimizing objective functions with multiple expectation constraints.
method Extended cooperative stochastic approximation algorithm.
result Achieves optimal convergence rate for optimality gap and constraint violation.